Data Processing Workflow¶

In this workflow within PyCCAPT, we can crop the data, do the voltage and bowl calibration, calculate the 3d reconstruction, and do the ranging.

In [1]:
# Activate intractive functionality of matplotlib
%matplotlib ipympl
# Activate auto reload 
%load_ext autoreload
%autoreload 2
%reload_ext autoreload
# import libraries
import os
import numpy as np
import pandas as pd
import subprocess
from ipywidgets import widgets
from IPython.display import display
from ipywidgets import fixed, interact_manual
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore")

# Local module and scripts
from pyccapt.calibration.calibration import widgets as wd
from pyccapt.calibration.data_tools import data_tools, data_loadcrop, dataset_path_qt
from pyccapt.calibration.tutorials.tutorials_helpers import helper_calibration
from pyccapt.calibration.tutorials.tutorials_helpers import helper_data_loader
from pyccapt.calibration.tutorials.tutorials_helpers import helper_temporal_crop
from pyccapt.calibration.tutorials.tutorials_helpers import helper_special_crop
from pyccapt.calibration.tutorials.tutorials_helpers import helper_t_0_tune
from pyccapt.calibration.tutorials.tutorials_helpers import helper_mc_plot
from pyccapt.calibration.tutorials.tutorials_helpers import helper_3d_reconstruction
from pyccapt.calibration.tutorials.tutorials_helpers import helper_ion_selection
from pyccapt.calibration.tutorials.tutorials_helpers import helper_visualization
from pyccapt.calibration.tutorials.tutorials_helpers import helper_ion_list
from pyccapt.calibration.calibration import ion_selection
from pyccapt.calibration.calibration import share_variables
from pyccapt.calibration.leap_tools.leap_tools import read_rrng
In case of recieving the error about pytable library, you have to install the pytables library with conda command. to do that you can open a new cell and copy the line below in it. Then just run it like other cells. The pytables library will be innstalled.

!conda install --yes --prefix {sys.prefix} pytables

In the cell below we create variable object. This object is used in many of the functions to share the data between functions in as easy way.
In [2]:
variables = share_variables.Variables()

By clicking on the button below, you can select the dataset file you want to work with. The dataset file can be in various formats, including HDF5, EPOS, POS, ATO, and CSV. The cropped data will be saved in the same directory as the original dataset file in a new directory nammed load_crop. The name of the cropped dataset file will be the same as the original dataset file. The figures will be saved in the same directory as the dataset file.

In [3]:
button = widgets.Button(description='load dataset')

@button.on_click
def open_file_on_click_r(b):
    global dataset_path
    folder_path = variables.last_directory
    script = '..//..//data_tools//run_dataset_path_qt.py'
    cmd = f"python {script} {folder_path}"
    result = subprocess.run(cmd, capture_output=True, text=True, shell=True)
    dataset_path = result.stdout.strip()
    variables.last_directory = dataset_path

button
Out[3]:

ROI Selection and Data Cropping¶

From the dropdown lists below, you can select the instrument specifications of the dataset. The instrument specifications are the same as the ones used for the calibration process. Data mode is specify the dataset structure. The dataset can be in raw or calibrated mode. The flight path length is the distance between the sample and the detector. The t0 is the time of flight of the ions with the lowest mass-to-charge ratio. The maximum mass-to-charge ratio is the maximum mass-to-charge ratio of tat you want to plot. You can also change it in te related cells. The detector diameter is the diameter of the detector.

In [4]:
# create an object for selection of instrument specifications of the dataset
tdc, pulse_mode, flight_path_length, t0, max_mc, det_diam = wd.dataset_instrument_specification_selection()

# Display lists and comboboxes to selected instrument specifications
display(tdc, pulse_mode, flight_path_length, t0, det_diam, max_mc)
In [5]:
helper_data_loader.load_data(dataset_path, max_mc.value, flight_path_length.value, pulse_mode.value, tdc.value, variables)
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)
display(variables.data)
display(variables.range_data)

The maximum possible TOF is: 5010 ns
=============================
The data will be saved on the path: T:/Monajem/APT_data/2382_Jan-10-2025_15-12_NiC9_Al/2382_Jan-10-2025_15-12_NiC9_Al/data_processing/
=============================
The dataset name after saving is: 2382_Jan-10-2025_15-12_NiC9_Al
=============================
The figures will be saved on the path: T:/Monajem/APT_data/2382_Jan-10-2025_15-12_NiC9_Al/2382_Jan-10-2025_15-12_NiC9_Al/data_processing/
=============================
{'apt': ['experiment_chamber_vacuum', 'id', 'num_events', 'num_raw_signals', 'temperature', 'timestamps'], 'dld': ['high_voltage', 'laser_pulse', 'start_counter', 't', 'voltage_pulse', 'x', 'y'], 'tdc': ['channel', 'high_voltage', 'laser_pulse', 'start_counter', 'time_data', 'voltage_pulse']}
The data is loaded in raw mode
The number of data that is removed: 43356
Total number of Ions: 23050746
The maximum possible time of flight is: 5010
x (nm) y (nm) z (nm) mc (Da) mc_uc (Da) high_voltage (V) pulse t (ns) t_c (ns) x_det (cm) y_det (cm) delta_p multi start_counter
0 0.0 0.0 0.0 0.0 0.0 500.00 328.000 1560.517326 0.0 1.211429 0.786939 0.0 0.0 12877
1 0.0 0.0 0.0 0.0 0.0 500.00 328.000 3862.638198 0.0 1.315918 -0.538776 0.0 0.0 12897
2 0.0 0.0 0.0 0.0 0.0 500.00 328.000 4725.038556 0.0 1.795918 -0.884898 0.0 0.0 328
3 0.0 0.0 0.0 0.0 0.0 500.00 328.000 4286.572326 0.0 1.126531 -1.453061 0.0 0.0 5238
4 0.0 0.0 0.0 0.0 0.0 500.00 328.000 2820.023316 0.0 -3.983673 -0.153469 0.0 0.0 6942
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
23050741 0.0 0.0 0.0 0.0 0.0 4200.38 840.076 682.816770 0.0 2.390204 -2.259592 0.0 0.0 19720
23050742 0.0 0.0 0.0 0.0 0.0 4200.38 840.076 4706.048754 0.0 1.776327 -1.191837 0.0 0.0 19721
23050743 0.0 0.0 0.0 0.0 0.0 4200.38 840.076 3621.936114 0.0 -2.279184 1.423673 0.0 0.0 19852
23050744 0.0 0.0 0.0 0.0 0.0 4200.38 840.076 183.910986 0.0 2.491429 -2.357551 0.0 0.0 19921
23050745 0.0 0.0 0.0 0.0 0.0 4200.38 840.076 3537.884466 0.0 1.345306 -1.240816 0.0 0.0 19951

23050746 rows × 14 columns

name ion mass mc mc_low mc_up color element complex isotope charge
0 unranged0 un 0.0 0.0 0.0 400.0 #000000 [unranged] [0] [0] 0

Load a pre saved range file if you want from cell below

In [6]:
button_r = widgets.Button(description='load range dataset')

@button_r.on_click
def open_file_on_click_r(b):
    global range_path
    folder_path = variables.last_directory
    script = '..//..//data_tools//run_dataset_path_qt.py'
    cmd = f"python {script} {folder_path}"
    result = subprocess.run(cmd, capture_output=True, text=True, shell=True)
    range_path = result.stdout.strip()
    variables.last_directory = range_path

button_r
Out[6]:
In [7]:
if 'range_path' in globals():
    _, file_extension = os.path.splitext(range_path)
    if file_extension == '.rrng':
        variables.range_data = read_rrng(range_path)
    else:
        variables.range_data = pd.read_hdf(range_path, mode='r')
display(variables.range_data)
name ion mass mc mc_low mc_up color element complex isotope charge
0 unranged0 un 0.0 0.0 0.0 400.0 #000000 [unranged] [0] [0] 0

Temporal crop¶

Select the data by drawing a rectangle over the experiment history. Experiment history is a 2D histogram of the time of flight of the ions versus sequence of evaporation. The experiment history is plotted by clicking on the button below te cell.

In [8]:
helper_temporal_crop.call_plot_crop_experiment(variables, pulse_mode.value)

Spatial crop¶

Select the region of maximum concentration of Ions in the below plotted graph to utilize relevant data. To crop you can draw a circle over the filed desorption map. The field desorption map is a 2D histogram of the time of flight of the ions versus the position of the ions on the detector. The field desorption map is plotted by clicking on the button below the cell.

In [9]:
helper_special_crop.call_plot_crop_fdm(variables)

Calculate pulses since the last event pulse and ions per pulse. The percentage of loss in ROI selection process will be printed.

In [10]:
# maximum value of the tdc counter
if tdc.value == 'pyccapt':
    if not (variables.data['delta_p'] != 0).any() or not (variables.data['multi'] != 0).any():
        if (variables.data['start_counter'] != 0).any():
            max_start_counter = max(variables.data['start_counter'])
            # if (variables.data['pulse_pi'] == 0).all() or (variables.data['ion_pp'] == 0).all():
            pulse_pi, ion_pp = data_loadcrop.calculate_ppi_and_ipp(variables.data, max_start_counter)
            # add two calculated array to the croped dataset
            variables.data['delta_p'] = pulse_pi.astype(np.uintc)
            variables.data['multi'] = ion_pp.astype(np.uintc)
    
    # save the cropped data
    print('tof Crop Loss {:.2f} %'.format((100 - (len(variables.data) / len(variables.data_backup)) * 100)))
#percentage of double event per pulse
ion_pp = variables.data['multi'].to_numpy()
print('percentage of multihit event per pulse', len(ion_pp[ion_pp != 1]) / float(len(ion_pp)) * 100)

Progress: 0% complete
Progress: 19% complete
Progress: 39% complete
Progress: 59% complete
Progress: 79% complete
Progress: 99% complete
tof Crop Loss 27.82 %
percentage of multihit event per pulse 3.4706176948758403

In the next cell by changing the t0 value you can correct the position of H1 or any other known peak. this correction would be helpful for the position of the peaks in the m/c calibration process.

In [11]:
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)
variables.data

The maximum possible time of flight is: 5010
Out[11]:
x (nm) y (nm) z (nm) mc (Da) mc_uc (Da) high_voltage (V) pulse t (ns) t_c (ns) x_det (cm) y_det (cm) delta_p multi start_counter
0 0.0 0.0 0.0 0.0 0.0 3680.84 736.168 723.306402 0.0 0.943673 -2.778776 0 1 18197
1 0.0 0.0 0.0 0.0 0.0 3680.84 736.168 742.426506 0.0 2.432653 1.812245 39 1 18236
2 0.0 0.0 0.0 0.0 0.0 3680.84 736.168 721.633050 0.0 1.737143 0.300408 400 1 18636
3 0.0 0.0 0.0 0.0 0.0 3680.84 736.168 731.357694 0.0 0.212245 1.782857 37 1 18673
4 0.0 0.0 0.0 0.0 0.0 3680.84 736.168 726.989148 0.0 1.564082 -2.527347 405 1 19078
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16636947 0.0 0.0 0.0 0.0 0.0 5234.12 1046.824 611.843328 0.0 1.469388 -2.383673 307 1 4887
16636948 0.0 0.0 0.0 0.0 0.0 5234.12 1046.824 610.910640 0.0 2.063673 -1.753469 86 1 4973
16636949 0.0 0.0 0.0 0.0 0.0 5234.12 1046.824 609.154992 0.0 -0.862041 -2.726531 15 1 4988
16636950 0.0 0.0 0.0 0.0 0.0 5234.12 1046.824 618.481872 0.0 1.596735 1.126531 406 1 5394
16636951 0.0 0.0 0.0 0.0 0.0 5234.12 1046.824 608.729796 0.0 1.338776 0.535510 383 1 5777

16636952 rows × 14 columns

In [12]:
helper_t_0_tune.call_fine_tune_t_0(variables, flight_path_length, pulse_mode, t0)

Remove the data with m/c greater than max m/c and x, y, t = 0. Also add the needed colums for calibration. The data types of the final cropped dataset is displayed below.

In [13]:
# add the columns in the dataset for x, y, z, mc, mc calibrated, and t calibrated
helper_data_loader.add_columns(variables, max_mc)
# save data temporarily
data_tools.save_data(variables.data, variables, hdf=True, epos=False, pos=False, csv=False, temp=True)
display(variables.data)
display(variables.data.dtypes)

The number of data over max_mc: 0
The number of data with having t, x, and y equal to zero is: 0
x (nm) y (nm) z (nm) mc (Da) mc_uc (Da) high_voltage (V) pulse t (ns) t_c (ns) x_det (cm) y_det (cm) delta_p multi start_counter
0 0.0 0.0 0.0 0.0 29.403503 3680.84 736.168 723.306402 0.0 0.943673 -2.778776 0 1 18197
1 0.0 0.0 0.0 0.0 30.936210 3680.84 736.168 742.426506 0.0 2.432653 1.812245 39 1 18236
2 0.0 0.0 0.0 0.0 30.556896 3680.84 736.168 721.633050 0.0 1.737143 0.300408 400 1 18636
3 0.0 0.0 0.0 0.0 31.408325 3680.84 736.168 731.357694 0.0 0.212245 1.782857 37 1 18673
4 0.0 0.0 0.0 0.0 29.671481 3680.84 736.168 726.989148 0.0 1.564082 -2.527347 405 1 19078
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16636947 0.0 0.0 0.0 0.0 29.424786 5234.12 1046.824 611.843328 0.0 1.469388 -2.383673 307 1 4887
16636948 0.0 0.0 0.0 0.0 29.444546 5234.12 1046.824 610.910640 0.0 2.063673 -1.753469 86 1 4973
16636949 0.0 0.0 0.0 0.0 29.071973 5234.12 1046.824 609.154992 0.0 -0.862041 -2.726531 15 1 4988
16636950 0.0 0.0 0.0 0.0 31.084819 5234.12 1046.824 618.481872 0.0 1.596735 1.126531 406 1 5394
16636951 0.0 0.0 0.0 0.0 30.466620 5234.12 1046.824 608.729796 0.0 1.338776 0.535510 383 1 5777

16636952 rows × 14 columns

x (nm)              float64
y (nm)              float64
z (nm)              float64
mc (Da)             float64
mc_uc (Da)          float64
high_voltage (V)    float64
pulse               float64
t (ns)              float64
t_c (ns)            float64
x_det (cm)          float64
y_det (cm)          float64
delta_p              uint32
multi                uint32
start_counter        uint32
dtype: object

Time-of-Flight/Mass-to-charge Calibration¶

Below you can plot the ToF histogram of the dataset. You can select the peak range of the data you want to plot by drawing a rectangle over peak with holding left click. After that you should apply the voltage and bowl calibration. These three steps should be repeated until you see no improvement in the peak resolution. You can also save the calibration by clicking on the save button. The saved calibration will be used for the next steps.

In [14]:
# exctract needed data from Pandas data frame as an numpy array
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)

The maximum possible time of flight is: 5010
In [15]:
helper_calibration.call_voltage_bowl_calibration(variables, det_diam.value, flight_path_length.value, pulse_mode.value)
In [16]:
variables.dld_t_calib_backup = np.copy(variables.dld_t_calib)
variables.mc_calib_backup = np.copy(variables.mc_calib)
In [17]:
helper_ion_list.call_ion_list(variables, selector='peak')
In [18]:
variables.data['mc (Da)'] = variables.mc_calib
variables.data['t_c (ns)'] = variables.dld_t_calib
# Remove negative mc
threshold = 0
mc_t = variables.data['mc (Da)'].to_numpy()
mc_t_mask = (mc_t <= threshold)
print('The number of ions with negative mc are:', len(mc_t_mask[mc_t_mask==True]))
variables.data.drop(np.where(mc_t_mask)[0], inplace=True)
variables.data.reset_index(inplace=True, drop=True)
# save data temporarily
data_tools.save_data(variables.data, variables, hdf=True, epos=False, pos=False, csv=False, temp=True)

The number of ions with negative mc are: 2489
In [19]:
helper_mc_plot.call_mc_plot(variables, selector='None')

3D Reconstruction¶

After bowl and voltage calibration we are ready to calculate the 3d reconstruction. In this workflow we calculate the reconstructed x,y,z and then plot the 3d, heatmap, projection plots and mass-to-charge histogram.

In [20]:
# exctract needed data from Pandas data frame as a numpy array
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)

display(variables.data)

The maximum possible time of flight is: 5010
x (nm) y (nm) z (nm) mc (Da) mc_uc (Da) high_voltage (V) pulse t (ns) t_c (ns) x_det (cm) y_det (cm) delta_p multi start_counter
0 0.0 0.0 0.0 27.023671 29.403503 3680.84 736.168 723.306402 656.477023 0.943673 -2.778776 0 1 18197
1 0.0 0.0 0.0 26.935078 30.936210 3680.84 736.168 742.426506 655.460971 2.432653 1.812245 39 1 18236
2 0.0 0.0 0.0 26.913140 30.556896 3680.84 736.168 721.633050 655.209121 1.737143 0.300408 400 1 18636
3 0.0 0.0 0.0 27.041236 31.408325 3680.84 736.168 731.357694 656.678267 0.212245 1.782857 37 1 18673
4 0.0 0.0 0.0 27.171525 29.671481 3680.84 736.168 726.989148 658.169010 1.564082 -2.527347 405 1 19078
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16634458 0.0 0.0 0.0 27.188185 29.424786 5234.12 1046.824 611.843328 658.359375 1.469388 -2.383673 307 1 4887
16634459 0.0 0.0 0.0 26.975280 29.444546 5234.12 1046.824 610.910640 655.922243 2.063673 -1.753469 86 1 4973
16634460 0.0 0.0 0.0 27.040801 29.071973 5234.12 1046.824 609.154992 656.673286 -0.862041 -2.726531 15 1 4988
16634461 0.0 0.0 0.0 27.423863 31.084819 5234.12 1046.824 618.481872 661.046094 1.596735 1.126531 406 1 5394
16634462 0.0 0.0 0.0 27.048862 30.466620 5234.12 1046.824 608.729796 656.765623 1.338776 0.535510 383 1 5777

16634463 rows × 14 columns

You have to select the main element in your sample from the from dropdown below.

In [21]:
element_selected = wd.density_field_selection()
display(element_selected)

In case that yopu face error about plotly library, like javascripts error. You have check your jupyter lab version is compatibale with the plotly extenstion. Sometimes running jupyter lab build command fix the proble.

In [22]:
helper_3d_reconstruction.call_x_y_z_calculation(variables, flight_path_length, element_selected=element_selected)
In [23]:
variables.data['x (nm)'] = variables.x
variables.data['y (nm)'] = variables.y
variables.data['z (nm)'] = variables.z
# save data temporarily
data_tools.save_data(variables.data, variables, hdf=True, epos=False, pos=False, csv=False, temp=True)

Ion Selection and Rangging¶

This tutorial outlines a comprehensive workflow for ion selection and organization. Users can choose ions using peak and element finders, manually add ions, customize ion colors, and create histograms. Histograms can be generated for selected ranges and areas, and figures can be saved. Additionally, users have the option to save both figures and data in CSV and HDF5 formats.

In [24]:
# exctract needed data from Pandas data frame as an numpy array
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)

display(variables.data)

The maximum possible time of flight is: 5010
x (nm) y (nm) z (nm) mc (Da) mc_uc (Da) high_voltage (V) pulse t (ns) t_c (ns) x_det (cm) y_det (cm) delta_p multi start_counter
0 5.544648 -16.326975 3.173361 27.023671 29.403503 3680.84 736.168 723.306402 656.477023 0.943673 -2.778776 0 1 18197
1 14.248317 10.614518 3.376773 26.935078 30.936210 3680.84 736.168 742.426506 655.460971 2.432653 1.812245 39 1 18236
2 10.519766 1.819208 1.191354 26.913140 30.556896 3680.84 736.168 721.633050 655.209121 1.737143 0.300408 400 1 18636
3 1.284474 10.789582 1.234681 27.041236 31.408325 3680.84 736.168 731.357694 656.678267 0.212245 1.782857 37 1 18673
4 9.179021 -14.832072 3.250077 27.171525 29.671481 3680.84 736.168 726.989148 658.169010 1.564082 -2.527347 405 1 19078
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16634458 12.327791 -19.998417 203.316053 27.188185 29.424786 5234.12 1046.824 611.843328 658.359375 1.469388 -2.383673 307 1 4887
16634459 17.361259 -14.751576 203.062549 26.975280 29.444546 5234.12 1046.824 610.910640 655.922243 2.063673 -1.753469 86 1 4973
16634460 -7.219228 -22.833542 203.482930 27.040801 29.071973 5234.12 1046.824 609.154992 656.673286 -0.862041 -2.726531 15 1 4988
16634461 13.695249 9.662292 201.255914 27.423863 31.084819 5234.12 1046.824 618.481872 661.046094 1.596735 1.126531 406 1 5394
16634462 11.595761 4.638304 200.327160 27.048862 30.466620 5234.12 1046.824 608.729796 656.765623 1.338776 0.535510 383 1 5777

16634463 rows × 14 columns

In [25]:
display(variables.data.dtypes)

x (nm)              float64
y (nm)              float64
z (nm)              float64
mc (Da)             float64
mc_uc (Da)          float64
high_voltage (V)    float64
pulse               float64
t (ns)              float64
t_c (ns)            float64
x_det (cm)          float64
y_det (cm)          float64
delta_p              uint32
multi                uint32
start_counter        uint32
dtype: object
In [26]:
helper_ion_selection.call_ion_selection(variables)

Now we can also show the color in the range dataframe

In [28]:
display(variables.range_data.style.applymap(ion_selection.display_color, subset=['color']))
  name ion mass mc mc_low mc_up color element complex isotope charge
0 H1 ${}^{1}H^{+}$ 1.010000 1.002499 0.847339 1.178480 #b2aa2d ['H'] [1] [1] 1.000000
1 Al1 ${}^{27}Al^{2+}$ 13.490000 13.433483 13.253794 14.185893 #e7e0d1 ['Al'] [1] [27] 2.000000
2 Al1 ${}^{27}Al^{+}$ 26.980000 26.967215 26.542237 28.995128 #e7e0d1 ['Al'] [1] [27] 1.000000
In [29]:
variables.range_data.dtypes
Out[29]:
name        object
ion         object
mass       float64
mc         float64
mc_low     float64
mc_up      float64
color       object
element     object
complex     object
isotope     object
charge     float64
dtype: object

Save the range in the hdf5 and csv format.

In [30]:
interact_manual_range = interact_manual.options(manual_name="save range")
interact_manual_range(data_tools.save_range, variables=fixed(variables));

Save the cropped dataset. You can specify te output format from list below. The output formats are HDF5, EPOS, POS, ATO, and CSV. The output file will be saved in the same directory as the original dataset file in a new directory nammed load_crop.

In [31]:
interact_manual_data = interact_manual.options(manual_name="save data")
interact_manual_data(data_tools.save_data, data=fixed(variables.data), variables=fixed(variables),
                name=widgets.Text(value=variables.result_data_name),
                hdf=widgets.Dropdown(options=[('True', True), ('False', False)]),
                epos=widgets.Dropdown(options=[('False', False), ('True', True)]),
                pos=widgets.Dropdown(options=[('False', False), ('True', True)]),
                ato_6v=widgets.Dropdown(options=[('False', False), ('True', True)]),
                csv=widgets.Dropdown(options=[('False', False), ('True', True)]),
               temp=fixed(False));

Visualization¶

In [32]:
variables.data
Out[32]:
x (nm) y (nm) z (nm) mc (Da) mc_uc (Da) high_voltage (V) pulse t (ns) t_c (ns) x_det (cm) y_det (cm) delta_p multi start_counter
0 5.544648 -16.326975 3.173361 27.023671 29.403503 3680.84 736.168 723.306402 656.477023 0.943673 -2.778776 0 1 18197
1 14.248317 10.614518 3.376773 26.935078 30.936210 3680.84 736.168 742.426506 655.460971 2.432653 1.812245 39 1 18236
2 10.519766 1.819208 1.191354 26.913140 30.556896 3680.84 736.168 721.633050 655.209121 1.737143 0.300408 400 1 18636
3 1.284474 10.789582 1.234681 27.041236 31.408325 3680.84 736.168 731.357694 656.678267 0.212245 1.782857 37 1 18673
4 9.179021 -14.832072 3.250077 27.171525 29.671481 3680.84 736.168 726.989148 658.169010 1.564082 -2.527347 405 1 19078
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16634458 12.327791 -19.998417 203.316053 27.188185 29.424786 5234.12 1046.824 611.843328 658.359375 1.469388 -2.383673 307 1 4887
16634459 17.361259 -14.751576 203.062549 26.975280 29.444546 5234.12 1046.824 610.910640 655.922243 2.063673 -1.753469 86 1 4973
16634460 -7.219228 -22.833542 203.482930 27.040801 29.071973 5234.12 1046.824 609.154992 656.673286 -0.862041 -2.726531 15 1 4988
16634461 13.695249 9.662292 201.255914 27.423863 31.084819 5234.12 1046.824 618.481872 661.046094 1.596735 1.126531 406 1 5394
16634462 11.595761 4.638304 200.327160 27.048862 30.466620 5234.12 1046.824 608.729796 656.765623 1.338776 0.535510 383 1 5777

16634463 rows × 14 columns

helper_visualization.call_visualization(variables)¶

In [33]:
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)

The maximum possible time of flight is: 5010
In [34]:
helper_visualization.call_visualization(variables)
In [35]:
variables.plotly_3d_reconstruction.show()
In [ ]:
from IPython.display import display, HTML
display(HTML(variables.animation_detector_html))